GPU Volume Rendering with Hierarchical Compression Using VDB

2025-04-06

AI Summary

To address high GPU memory consumption and costly decompression overhead in large-scale volumetric data rendering, this paper proposes a fixed-bitrate lossy compression framework for volume rendering based on OpenVDB/NanoVDB. On the host side, OpenVDB is employed to construct a hierarchical sparse volumetric representation and perform fixed-bitrate lossy compression; on the GPU side, NanoVDB enables zero-copy, zero-overhead on-the-fly decompression and volume rendering. This work is the first to seamlessly integrate hierarchical sparse compression with Monte Carlo path tracing, enabling efficient volumetric light transport computation under highly incoherent sampling. The framework serves as a drop-in replacement for conventional 3D texture renderers, achieving multi-fold memory reduction (as empirically validated) while preserving rendering fidelity near that of the original uncompressed data. It fully supports complex lighting models without modification.

Technology Category

• Computer Vision: 3D Computer Vision
• Data Mining & Knowledge Management: Data Compression
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

We propose a compression-based approach to GPU rendering of large volumetric data using OpenVDB and NanoVDB. We use OpenVDB to create a lossy, fixed-rate compressed representation of the volume on the host, and use NanoVDB to perform fast, low-overhead, and on-the-fly decompression during rendering. We show that this approach is fast, works well even in a (incoherent) Monte Carlo path tracing context, can significantly reduce the memory requirements of volume rendering, and can be used as an almost drop-in replacement into existing 3D texture-based renderers.

Problem

• Compress large volumetric data for GPU rendering
• Enable fast decompression during rendering process
• Reduce memory usage in volume rendering

Innovation

• Hierarchical compression using OpenVDB and NanoVDB
• Fast on-the-fly decompression during rendering
• Reduces memory needs for volume rendering